June 17 ~ 18, 2023, Sydney, Australia
Frederick Abban, Dr. Koudjo M. KoumadiDepartment of Computer Engineering, University of Ghana, Legon, Ghana
The universal exponential increase in the demand for high data rate for mobile devices has propelled lots of research in wireless communication. Deploying and implementing a wireless network in a particular geographical area requires proper planning since all existing propagation models are not “one size fit all” models. In this study, path losses of seven empirical propagation model were simulated and compared with results of measurements of received signal strength in Non-Line of Sight (NLOS) scenario for Accra, Ghana on 2300 MHz. The study terrain is similar to most cities on the coast of West Africa. Correction factors were computed and applied to original propagation model equations and the Ericsson 9999 model showed the best fit to the measurement data, thus it can be used to predict received signal strength for Accra and other environments with similar terrains.
4G, Propagation Models, Received Signal Strength, LTE, Path Loss, NLOS.
Seyyed Rohollah Mirhoseini1, ehrouz Minaei-Bidgoli 2, Rahil Hosseini3 and Bahareh Shaker-Ardakani1, 1Department of Computer Engineering Islamic Azad University North Tehran branch Tehran, Iran, 2Department of Computer Engineering Iran University of Science and Technology Tehran, Iran, 3Department of Computer Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran
Today there are more than 4.39 billion internet users that almost 70 percent of them use social media on mobile devices. Network security is one of the most important aspects to consider while working over the internet, LAN or other networks, no matter how small or big your business is. We know many zero day attacks are continuously emerging because of the addition of various protocols mainly from Internet of Things (IoT). previously known attacks in cyberattacks can detect by using Artificial Intelligence (AI) solutions such as Neural Networks, Machine Learning (ML), Support Vector Machine (SVM), decision tree, Hidden Markov Model (HMM), Hierarchical Clustering, Game Theory (GT), and Natural Language Processing (NLP). But advanced mechanisms of AI are not able to detect all of attacks. On the other hand Deep Learning (DL) techniques which are capable of providing embedded intelligence in the IoT devices and networks, are emerged to cope with different security problems. Then we used Convolutional Neural Network (CNN) and Genetic Algorithm.
Network security, Convolutional Neural Network (CNN), Deep Learning and Genetic Algorithm.
Chaitanyateja Thotadi1, Monith Debbalav1, Subba Rao1, Alavalapati Goutham Reddy2*, Basker Palaniswamy3, Vanga Odelu1, 1National Institute of Technology, Andhra Pradesh, India, 2Fontbonne University, St. Louis, USA, 3Queensland University of Technology, Brisbane, Australia, 4Indian Institute of Infromation Technology Sri City, Chittoor, India
Social network providers offer a variety of entertainment services in exchange for end users’ personal information, such as their identity. The majority of users access social networking sites via their smartphones, which they utilize in conjunction with a traditional authenticator like a password. On the other hand, aggregators, which pull content from multiple social networks, are often used to get into smartphone apps that may involve mobile ticketing, identification, and access control. They are a potential target for malware and spyware injections due to their powerful position. Malware is capable of circumventing authentication mechanisms in order to get access to social networking services, which may result in stealing the personal information of users. To deflect any type of attack from malicious software, BrightPass [22], a malware-resistant method based on screen brightness, was introduced. Conversely, we have demonstrated that the BrightPass user’s personally identifiable information, such as PIN numbers, may be recovered by evaluating the variations between the recorded input from many authentication sessions. We have then offered various enhanced BrightPass versions to address the observed vulnerability. Our enhanced BrightPass versions are both simple and secure to use when it comes to accessing social networks via mobiles.
Smartphones, Social Networks, Malware, Authentication, Security.
Fatai Jimoh1, Mo Saraee2, Azar Shahgholian3, 1,2School of Engineering Science and Humanity, University of Salford, United Kingdom, 3Liverpool Business School, Liverpool John Moore University, Liverpool, United Kingdom
The conventional data mining approach to crime prediction models often depends on historical data. However, considering the current global crime trend where offenders frequently register their criminal intent on social media and also invite others to witness and/or participate in various crimes, there is a need for an alternative and more dynamic approach.This paper, therefore, applied an ensemble of machine learning algorithms to reducethe crime rate in Manchester cities by combining historical crime data with tweet data. To overcome the problem with data qualities and bias in our prediction we ensured that all the features used in this work were from the same year and same month. The ensembled method showed the highest performance in predicting different categories of crimes with the highest accuracy when compared with base models and our study also revealed the contribution of sentiment score to the overall performance of the model. Finally, we conclude that social media data if properly mined would contribute to an improved prediction on the likelihood of crimes occurrence as well as their prediction.
Data Mining, Crime Prediction, Machine learning, Sentiment Analysis, Stacked Ensemble.
Ariestelo A. Asilo1,2, Czarina Anne A. Villareiz1, Arnan B. Araza3, Jose Arnold S. Bagabaldo4, and Francis G. Balazon5, 1Varacco Inc & 2ThinnkFarm, 3Wageningen University and Research, 4Packetworx, 5Batangas State University, The National Engineering University Lipa Campus
This study focuses on the use of Internet of Things (IoT) devices for monitoring microclimate conditions in coffee production, specifically in the highland and lowland areas of the Philippines. The aim is to assess the effectiveness and accuracy of IoT-based data in decision-making for farm interventions, compared to data from the well-known weather prediction app AccuWeather. This comparison is important in developing prediction models useful for upscaling the study to other farms. The results show that there are systematic differences in the temperature, relative humidity, and amount of rainfall data collected by the two methods, indicating the importance of on-site IoT devices for localized microclimate monitoring. However, the study also highlights some challenges in the implementation of IoT, such as malfunctions and hardware issues. This research underscores the need for further testing and calibration of IoT devices in coffee production, as well as the establishment of a localized Good Agricultural Practices (GAP) reinforced by IoT-based microclimate conditions.
Coffee, IoT, Microclimate, Wireless Sensor.
Josaphat Rutaganda1, Mariantonietta Fiore2, Nino Adamashvili3, 1Department of Agronomy, Institut Polytechnique UniLaSalle, Rouen, France, 2, 3Department of Economics, the University of Foggia, Foggia, Italy
The paper presents a systematic literature reviewthat investigates the potential of integrating the Internet of Things (IoT), Artificial Intelligence (AI), and Blockchain technology (BCT) to improve the safety and sustainability of the wine supply chain. The study identifies the challenges and opportunities of these technologies and their potential for addressing issues such as food safety, quality control, and traceability in the wine industry. Additionally, the framework is proposed by authors in order to overcome the challenges associated with the implementation of these technologies in the wine supply chain. The findings suggest that the integration of IoT, AI, and BCT can lead to increased transparency, efficiency, and security in the wine production chain.The study concludes that the integration of these technologies can contribute to a more sustainable and safer wine supply chain.
BCT, IoT, AI, wine, supply chain.